Cloudera Fast Forward Labs is a machine intelligence research company.

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New Research on Interpretability

An interpretable algorithm is one whose decisions you can explain. You can
better rely on such a model to be safe, accurate and useful.

Our prototype shows how new ideas in interpretability research can be used to
extract actionable insight from black-box machine learning models.

And our report describes breakthroughs in interpretability research and places
them in a commercial, legal and ethical context.

This research is relevant to anyone who designs systems using machine learning,
from engineers and data scientists, to business leaders and executives who are
considering new product opportunities.

The Power of Interpretability

A model you can interpret and understand is one you can more easily improve. It
is also one you, regulators, and society can more easily trust to be safe and
nondiscriminatory. And an accurate model that is also interpretable can offer
insights that can be used to change real-world outcomes for the better.

There is a central tension, however, between accuracy and interpretability: the
most accurate models are necessarily the hardest to understand. Our report
looks closely at two recent breakthroughs that resolve this tension. New
white-box algorithms offer better performance while guaranteeing
interpretability. Meanwhile, model-agnostic interpretability techniques allow
you to peer inside black-box models.

Our report explains how these techniques work at both a conceptual and
technical level, and then discusses the commercial opportunities for their
application.

Our prototype, meanwhile, makes these possibilities concrete. We applied a
model-agnostic tool called LIME to a black-box model, in order to better
understand the reasons a subscription business loses customers. An accurate
model that predicts which customers your business is about to lose is useful.
But it’s much more useful if you can also see why they are about to leave. In
this way, you learn about weaknesses in your business, and can perhaps even
intervene to prevent the losses.

More Important than Ever

Work on machine learning interpretability is more important than ever. Our
society is increasingly dependent on intelligent machines. Algorithms govern
everything from which e-mails reach our inboxes to whether we are approved for
credit to whom we get the opportunity to date. And their impact on our
experience of the world is growing.

This rise in the use of algorithms coincides with a surge in the capabilities
of black-box techniques, or algorithms whose inner workings cannot easily be
explained. The question of interpretability has been important in applied
machine learning for many years, but as relatively uninterpretable techniques
like deep learning grow in popularity, it’s becoming an urgent concern. These
techniques offer breakthrough capabilities in analyzing and even generating
rich media and text data, but it’s often hard to figure out how they do what
they do.

The future is algorithmic. Interpretable models offer a safer, more productive,
and ultimately more collaborative relationship between humans and intelligent
machines.

Learn More

We will host a public webinar on
interpretability on September 6 2017. We’ll be joined by guests Patrick Hall (Senior Data Scientist at H2O,
co-author of Ideas on Interpreting Machine
Learning)
and Sameer Singh (Assistant Professor of Computer Science at UC Irvine,
co-creator of LIME, a model-agnostic tool for extracting explanations from
black box machine learning models).

How to Access our Reports and Prototypes

We offer our research on interpretability in a few ways:

Annual research subscription (for individuals and corporate members)

Subscription and advising (research and time with our team)

Special projects and workshops (help to build a great data product or strategy)